Characterization and Modeling of the Magnetomechanical Behavior of Iron-Gallium Alloys
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Magnetostrictive Iron-Gallium alloys (Galfenol) demonstrate moderate magnetostriction (~350 ppm) under very low magnetic fields (~100 Oe), have very low hysteresis, high tensile strength (~500 MPa), high Curie temperature (~675°C), are in general machinable, ductile and corrosion resistant. Therefore, they hold great promise in active vibration control, actuation, stress and torque sensing in helicopters, aircrafts and automobiles. To facilitate design of magnetostrictive actuators and sensors using this material, as well as to aid in making it commercially viable, it is necessary to perform a comprehensive characterization and modeling of its magnetomechanical behavior. This dissertation addresses some of these issues, focusing primarily on quasi-static characterization and modeling of the magnetomechanical behavior of single-crystal FeGa alloys with varying gallium content and along different crystallographic directions, and studying the effect of texture on the magnetomechanical behavior of polycrystals. Additionally, improved testing and modeling paradigms for magnetostrictive materials are developed to contribute to a better understanding and prediction of actuation and sensing behavior of FeGa alloys. In particular, the actuation behavior (λ-H and B-H curves) for 19, 24.7 and 29 at. % Ga <100> oriented single crystal FeGa samples are characterized and the strikingly different characteristics are simulated and explained using an energy based model. Actuation and sensing (B-σ and є-σ curves) behavior of <100> oriented 19 at. % Ga and <110> oriented 18 at. % Ga single crystal samples are characterized. It is demonstrated that the sensing behavior can be predicted by the model, using parameters obtained from the actuation behavior. The actuation and sensing behavior of 18.4 at. % Ga polycrystalline FeGa sample is predicted from the volume fraction of grains close to the , , , , ,  and  orientations (obtained from cross-section texture analysis). The predictions are benchmarked against experimental actuator and sensor characteristics of the polycrystalline sample.